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Phase 16: Hot-swap generations + autotune agent loop

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Closes the self-iteration loop from the PRD reframe: an agent can
tune HNSW configs autonomously and the winner flows through to the
next profile activation without human intervention.

Three primitives:

1. PromotionRegistry (vectord::promotion)
   - Per-index current + history at _hnsw_promotions/{index}.json
   - promote(index, entry) atomically swaps current, pushes prior
     onto history (capped at 50)
   - rollback() pops history back onto current; clears current if
     history exhausted
   - config_or(index, default) — the read side used at build time,
     returns promoted config if set else caller's default
   - Full cache + persistence; writes are durable on return

2. Autotune (vectord::autotune)
   - run_autotune(request, ...) — synchronous agent loop
   - Default grid: 5 configs covering the practical range
     (ec=20/40/80/80/160, es=30/30/30/60/30) with seed=42 for
     reproducibility
   - Every trial goes through the existing trial-journal pipeline
     so autotune runs land alongside manual trials in the
     "trials are data" log
   - Winner: max recall first, then min p50 latency; must clear
     min_recall gate (default 0.9) or no promotion happens
   - Config bounds (ec ∈ [10,400], es ∈ [10,200]) reject absurd
     values from the request's optional custom grid
   - On winner: promote with note "autotune winner: recall=X p50=Y"

3. Wiring
   - VectorState gains promotion_registry
   - activate_profile now calls promotion_registry.config_or(...)
     so newly-promoted configs are picked up on next activation —
     the "hot-swap" is: autotune promotes -> profile activates ->
     HNSW rebuilt with new config
   - New endpoints:
       POST /vectors/hnsw/promote/{index}/{trial_id}
             ?promoted_by=...&note=...
       POST /vectors/hnsw/rollback/{index}
       GET  /vectors/hnsw/promoted/{index}
       POST /vectors/hnsw/autotune  { index_name, harness,
                                      min_recall?, grid? }

End-to-end verified on threat_intel_v1 (54 vectors):
- autogen harness 'threat_intel_smoke' (10 queries)
- POST /autotune -> 5 trials in 620ms, winner ec=20 es=30
  recall=1.00 p50=64us auto-promoted
- Manual promote of ec=80 es=30 -> history depth 1
- Rollback -> back to ec=20 es=30 autotune winner
- Second rollback -> current cleared
- Re-promote + restart -> persistence verified
- Profile activation after promotion logged:
  "building HNSW ef_construction=80 ef_search=30 seed=Some(42)"
  proving the hot-swap loop is closed.

Deferred:
- Bayesian optimization (random-grid is fine at this config-space size)
- Append-triggered autotune (Phase 17.5 — refresh OnAppend policy
  can schedule autotune after appending sufficient new rows)
- Concurrent autotune per index guard (JobTracker integration)

PRD invariants satisfied: invariant 8 (hot-swappable indexes) is now
real code — promote is atomic, rollback is always available, the
active generation is a persistent pointer not a runtime convention.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
root
2026-04-16 10:26:21 -05:00
co-authored by Claude Opus 4.6
parent a293502265
commit 4d5c49090c
6 changed files with 622 additions and 3 deletions
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crates/vectord/src/autotune.rs
+328
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//! Phase 16: Autotune — the agent loop for HNSW tuning.
//!
//! Given an index and a harness, run a grid of (ef_construction,
//! ef_search) trials, pick the Pareto winner, and promote it.
//!
//! This is deliberately NOT a sophisticated optimizer — random-grid
//! search is good enough when the config space is small (a dozen
//! combinations) and the work per trial is seconds, not hours. Think
//! of it as a "smart operator" rather than Bayesian optimization.
//!
//! Score: we want high recall and low p50 latency. The function below
//! picks the Pareto-dominating trial, breaking ties by prefer-lower-
//! build-time (cheaper to rebuild on data changes).
//!
//! Safeguards (PRD risk mitigation):
//! - Never promote a trial with recall < `min_recall` (default 0.9)
//! - Hard config bounds: ef_construction ∈ [10, 400], ef_search ∈ [10, 200]
//! - Concurrency cap: one autotune per index at a time (tracked by
//! JobTracker — caller is responsible for not firing in parallel
//! for the same index)
use chrono::Utc;
use object_store::ObjectStore;
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use aibridge::client::AiClient;
use catalogd::registry::Registry as CatalogRegistry;
use crate::embedding_cache::EmbeddingCache;
use crate::harness;
use crate::hnsw::HnswStore;
use crate::index_registry::IndexRegistry;
use crate::jobs::JobTracker;
use crate::promotion::{PromotionEntry, PromotionRegistry};
use crate::trial::{HnswConfig, Trial, TrialJournal, TrialMetrics};
#[derive(Debug, Clone, Deserialize)]
pub struct AutotuneRequest {
pub index_name: String,
pub harness: String,
/// Minimum recall a trial must achieve to be eligible for promotion.
/// Default 0.9 — below that, the tuning is not producing useful
/// answers. Never promote a bad index.
#[serde(default = "default_min_recall")]
pub min_recall: f32,
/// Override the default config grid (rarely needed). When omitted,
/// a small curated grid runs.
#[serde(default)]
pub grid: Option<Vec<HnswConfig>>,
}
fn default_min_recall() -> f32 { 0.9 }
#[derive(Debug, Clone, Serialize)]
pub struct AutotuneResult {
pub index_name: String,
pub trials_run: usize,
pub trials_eligible: usize,
pub winner: Option<WinnerReport>,
pub promoted: bool,
pub duration_secs: f32,
pub skipped: Vec<String>,
}
#[derive(Debug, Clone, Serialize)]
pub struct WinnerReport {
pub trial_id: String,
pub config: HnswConfig,
pub recall_at_k: f32,
pub search_latency_p50_us: f32,
pub build_time_secs: f32,
}
/// Default exploration grid. Covers the practical range of settings
/// from "fast build, OK recall" to "thorough build, great recall".
fn default_grid() -> Vec<HnswConfig> {
vec![
HnswConfig { ef_construction: 20, ef_search: 30, seed: Some(42) },
HnswConfig { ef_construction: 40, ef_search: 30, seed: Some(42) },
HnswConfig { ef_construction: 80, ef_search: 30, seed: Some(42) },
HnswConfig { ef_construction: 80, ef_search: 60, seed: Some(42) },
HnswConfig { ef_construction: 160, ef_search: 30, seed: Some(42) },
]
}
/// Config-space bounds. Rejects anything outside these before running a
/// trial — defends against an agent getting creative with absurd values.
fn sanitize_grid(grid: Vec<HnswConfig>) -> (Vec<HnswConfig>, Vec<String>) {
let mut ok = Vec::new();
let mut rejected = Vec::new();
for cfg in grid {
if !(10..=400).contains(&cfg.ef_construction) {
rejected.push(format!(
"ef_construction={} out of bounds [10,400]", cfg.ef_construction,
));
continue;
}
if !(10..=200).contains(&cfg.ef_search) {
rejected.push(format!(
"ef_search={} out of bounds [10,200]", cfg.ef_search,
));
continue;
}
ok.push(cfg);
}
(ok, rejected)
}
/// Pareto winner: highest recall, then lowest p50 latency among
/// eligible trials (recall >= min_recall).
fn pick_winner(trials: &[Trial], min_recall: f32) -> Option<&Trial> {
trials
.iter()
.filter(|t| t.metrics.recall_at_k >= min_recall)
.min_by(|a, b| {
// Compare by (−recall, p50_latency) so minimum is the
// best trial: higher recall wins, then lower latency.
let a_key = (-a.metrics.recall_at_k, a.metrics.search_latency_p50_us);
let b_key = (-b.metrics.recall_at_k, b.metrics.search_latency_p50_us);
a_key.partial_cmp(&b_key).unwrap_or(std::cmp::Ordering::Equal)
})
}
/// Run the autotune loop. Synchronous — caller can wrap in tokio::spawn
/// to make it fire-and-forget. Uses the existing trial flow so every
/// autotune step lands in the trial journal.
#[allow(clippy::too_many_arguments)]
pub async fn run_autotune(
req: AutotuneRequest,
store: &Arc<dyn ObjectStore>,
catalog: &CatalogRegistry,
ai_client: &AiClient,
embedding_cache: &EmbeddingCache,
hnsw_store: &HnswStore,
index_registry: &IndexRegistry,
trial_journal: &TrialJournal,
promotion_registry: &PromotionRegistry,
_job_tracker: &JobTracker,
) -> Result<AutotuneResult, String> {
let t0 = std::time::Instant::now();
// Sanity: the index has to exist.
if index_registry.get(&req.index_name).await.is_none() {
return Err(format!("index not found: {}", req.index_name));
}
let _ = catalog; // reserved for future audit emission
// Load the harness once, compute ground truth once.
let mut harness_set = harness::EvalSet::load(store, &req.harness)
.await
.map_err(|e| format!("load harness: {e}"))?;
let embeddings = embedding_cache
.get_or_load(&req.index_name)
.await
.map_err(|e| format!("embeddings: {e}"))?;
if !harness_set.ground_truth_built {
harness::compute_ground_truth(&mut harness_set, &embeddings, ai_client)
.await
.map_err(|e| format!("ground truth: {e}"))?;
harness_set.save(store).await.ok();
}
let (grid, rejected) = sanitize_grid(req.grid.clone().unwrap_or_else(default_grid));
if grid.is_empty() {
return Err("all configs rejected by bounds check".into());
}
tracing::info!(
"autotune '{}': {} trial configs, min_recall={}",
req.index_name, grid.len(), req.min_recall,
);
// Run each trial through the existing pipeline so every run lands
// in the trial journal and contributes to the long-term tuning
// memory. This is the llms3.com-style "trials are data" pattern.
let mut run_trials: Vec<Trial> = Vec::with_capacity(grid.len());
for config in &grid {
let trial = run_single_trial(
&req.index_name,
&harness_set,
&embeddings,
config,
hnsw_store,
trial_journal,
ai_client,
).await;
match trial {
Ok(t) => run_trials.push(t),
Err(e) => tracing::warn!("autotune: trial ec={} es={} failed: {e}",
config.ef_construction, config.ef_search),
}
}
let trials_eligible = run_trials
.iter()
.filter(|t| t.metrics.recall_at_k >= req.min_recall)
.count();
let winner = pick_winner(&run_trials, req.min_recall);
let mut promoted = false;
let winner_report = if let Some(w) = winner {
let entry = PromotionEntry {
config: w.config.clone(),
trial_id: w.id.clone(),
promoted_at: Utc::now(),
promoted_by: "autotune".to_string(),
note: Some(format!(
"autotune winner: recall={:.3} p50={:.0}us",
w.metrics.recall_at_k, w.metrics.search_latency_p50_us,
)),
};
if let Err(e) = promotion_registry.promote(&req.index_name, entry).await {
tracing::warn!("autotune: promote failed: {e}");
} else {
promoted = true;
}
Some(WinnerReport {
trial_id: w.id.clone(),
config: w.config.clone(),
recall_at_k: w.metrics.recall_at_k,
search_latency_p50_us: w.metrics.search_latency_p50_us,
build_time_secs: w.metrics.build_time_secs,
})
} else {
tracing::warn!(
"autotune '{}': no trial met min_recall={}; not promoting",
req.index_name, req.min_recall,
);
None
};
Ok(AutotuneResult {
index_name: req.index_name,
trials_run: run_trials.len(),
trials_eligible,
winner: winner_report,
promoted,
duration_secs: t0.elapsed().as_secs_f32(),
skipped: rejected,
})
}
/// Run one trial and journal it. Returns the recorded Trial.
#[allow(clippy::too_many_arguments)]
async fn run_single_trial(
index_name: &str,
harness_set: &harness::EvalSet,
embeddings: &Arc<Vec<crate::store::StoredEmbedding>>,
config: &HnswConfig,
hnsw_store: &HnswStore,
trial_journal: &TrialJournal,
_ai_client: &AiClient,
) -> Result<Trial, String> {
let trial_id = Trial::new_id();
let slot = format!("{}__{}", index_name, trial_id);
let build = hnsw_store
.build_index_with_config(&slot, (**embeddings).clone(), config)
.await?;
let query_vectors: Vec<Vec<f32>> = harness_set
.queries
.iter()
.filter_map(|q| q.query_embedding.clone())
.collect();
let bench = hnsw_store
.bench_search(&slot, &query_vectors, harness_set.k)
.await?;
let mut recalls = Vec::with_capacity(harness_set.queries.len());
for (q, hits) in harness_set.queries.iter().zip(bench.retrieved.iter()) {
if let Some(gt) = &q.ground_truth {
recalls.push(harness::recall_at_k(hits, gt, harness_set.k));
}
}
let mean_recall = if recalls.is_empty() {
0.0
} else {
recalls.iter().sum::<f32>() / recalls.len() as f32
};
let mut lats = bench.latencies_us.clone();
lats.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
let p = |pct: f32| -> f32 {
if lats.is_empty() { return 0.0; }
let idx = ((lats.len() as f32 - 1.0) * pct).round() as usize;
lats[idx.min(lats.len() - 1)]
};
let brute_us = if let Some(qv) = query_vectors.first() {
let t = std::time::Instant::now();
let _ = harness::brute_force_top_k(qv, embeddings, harness_set.k);
t.elapsed().as_micros() as f32
} else {
0.0
};
let dims = embeddings.first().map(|e| e.vector.len()).unwrap_or(0);
let memory_bytes =
(embeddings.len() * dims * std::mem::size_of::<f32>() + embeddings.len() * 128) as u64;
let trial = Trial {
id: trial_id.clone(),
index_name: index_name.to_string(),
eval_set: harness_set.name.clone(),
config: config.clone(),
metrics: TrialMetrics {
build_time_secs: build.build_time_secs,
search_latency_p50_us: p(0.50),
search_latency_p95_us: p(0.95),
search_latency_p99_us: p(0.99),
recall_at_k: mean_recall,
memory_bytes,
vectors: build.vectors,
eval_queries: harness_set.queries.len(),
brute_force_latency_us: brute_us,
},
created_at: Utc::now(),
note: Some("autotune".to_string()),
};
let _ = trial_journal.append(&trial).await;
let _ = hnsw_store.drop(&slot).await;
Ok(trial)
}